Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases
The immune system is one of the most critical systems in humans that resists all diseases and protects the body from viruses, bacteria, etc. White blood cells (WBCs) play an essential role in the immune system. To diagnose blood diseases, doctors analyze blood samples to characterize the features of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/8/1853 |
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| author | Tariq S. Almurayziq Ebrahim Mohammed Senan Badiea Abdulkarem Mohammed Zeyad Ghaleb Al-Mekhlafi Gharbi Alshammari Abdullah Alshammari Mansoor Alturki Abdullah Albaker |
| author_facet | Tariq S. Almurayziq Ebrahim Mohammed Senan Badiea Abdulkarem Mohammed Zeyad Ghaleb Al-Mekhlafi Gharbi Alshammari Abdullah Alshammari Mansoor Alturki Abdullah Albaker |
| author_sort | Tariq S. Almurayziq |
| collection | DOAJ |
| description | The immune system is one of the most critical systems in humans that resists all diseases and protects the body from viruses, bacteria, etc. White blood cells (WBCs) play an essential role in the immune system. To diagnose blood diseases, doctors analyze blood samples to characterize the features of WBCs. The characteristics of WBCs are determined based on the chromatic, geometric, and textural characteristics of the WBC nucleus. Manual diagnosis is subject to many errors and differing opinions of experts and takes a long time; however, artificial intelligence techniques can help to solve all these challenges. Determining the type of WBC using automatic diagnosis helps hematologists to identify different types of blood diseases. This work aims to overcome manual diagnosis by developing automated systems for classifying microscopic blood sample datasets for the early detection of diseases in WBCs. Several proposed systems were used: first, neural network algorithms, such as artificial neural networks (ANNs) and feed-forward neural networks (FFNNs), were applied to diagnose the dataset based on the features extracted using the hybrid method between two algorithms, the local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM). All algorithms attained superior accuracy for WBC diagnosis. Second, the pre-trained convolutional neural network (CNN) models AlexNet, ResNet-50, GoogLeNet, and ResNet-18 were applied for the early detection of WBC diseases. All models attained exceptional results in the early detection of WBC diseases. Third, the hybrid technique was applied, consisting of a pair of blocks: the CNN models block for extracting deep features and the SVM algorithm block for the classification of deep features with superior accuracy and efficiency. These hybrid techniques are named AlexNet with SVM, ResNet-50 with SVM, GoogLeNet with SVM, and ResNet-18 with SVM. All techniques achieved promising results when diagnosing the dataset for the early detection of WBC diseases. The ResNet-50 model achieved an accuracy of 99.3%, a precision of 99.5%, a sensitivity of 99.25%, a specificity of 99.75%, and an AUC of 99.99%. |
| first_indexed | 2024-03-11T05:04:22Z |
| format | Article |
| id | doaj.art-c09206c841524e88b65742472d4f7b26 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T05:04:22Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c09206c841524e88b65742472d4f7b262023-11-17T19:01:48ZengMDPI AGElectronics2079-92922023-04-01128185310.3390/electronics12081853Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell DiseasesTariq S. Almurayziq0Ebrahim Mohammed Senan1Badiea Abdulkarem Mohammed2Zeyad Ghaleb Al-Mekhlafi3Gharbi Alshammari4Abdullah Alshammari5Mansoor Alturki6Abdullah Albaker7Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana’a, YemenDepartment of Computer Engineering, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaThe immune system is one of the most critical systems in humans that resists all diseases and protects the body from viruses, bacteria, etc. White blood cells (WBCs) play an essential role in the immune system. To diagnose blood diseases, doctors analyze blood samples to characterize the features of WBCs. The characteristics of WBCs are determined based on the chromatic, geometric, and textural characteristics of the WBC nucleus. Manual diagnosis is subject to many errors and differing opinions of experts and takes a long time; however, artificial intelligence techniques can help to solve all these challenges. Determining the type of WBC using automatic diagnosis helps hematologists to identify different types of blood diseases. This work aims to overcome manual diagnosis by developing automated systems for classifying microscopic blood sample datasets for the early detection of diseases in WBCs. Several proposed systems were used: first, neural network algorithms, such as artificial neural networks (ANNs) and feed-forward neural networks (FFNNs), were applied to diagnose the dataset based on the features extracted using the hybrid method between two algorithms, the local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM). All algorithms attained superior accuracy for WBC diagnosis. Second, the pre-trained convolutional neural network (CNN) models AlexNet, ResNet-50, GoogLeNet, and ResNet-18 were applied for the early detection of WBC diseases. All models attained exceptional results in the early detection of WBC diseases. Third, the hybrid technique was applied, consisting of a pair of blocks: the CNN models block for extracting deep features and the SVM algorithm block for the classification of deep features with superior accuracy and efficiency. These hybrid techniques are named AlexNet with SVM, ResNet-50 with SVM, GoogLeNet with SVM, and ResNet-18 with SVM. All techniques achieved promising results when diagnosing the dataset for the early detection of WBC diseases. The ResNet-50 model achieved an accuracy of 99.3%, a precision of 99.5%, a sensitivity of 99.25%, a specificity of 99.75%, and an AUC of 99.99%.https://www.mdpi.com/2079-9292/12/8/1853CNNneural networksWBCshybrid techniquesSVMLBP |
| spellingShingle | Tariq S. Almurayziq Ebrahim Mohammed Senan Badiea Abdulkarem Mohammed Zeyad Ghaleb Al-Mekhlafi Gharbi Alshammari Abdullah Alshammari Mansoor Alturki Abdullah Albaker Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases Electronics CNN neural networks WBCs hybrid techniques SVM LBP |
| title | Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases |
| title_full | Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases |
| title_fullStr | Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases |
| title_full_unstemmed | Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases |
| title_short | Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases |
| title_sort | deep and hybrid learning techniques for diagnosing microscopic blood samples for early detection of white blood cell diseases |
| topic | CNN neural networks WBCs hybrid techniques SVM LBP |
| url | https://www.mdpi.com/2079-9292/12/8/1853 |
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